Style transfer enables style information from one image (a “style image”) to be transferred to another image, a content image, to transform the content image based on style attributes of the style image. For instance, brush stroke and color information from an image of a painting can be transferred to a photograph to generate a stylized version of the photograph that includes the original content of the photograph transformed using the brush stroke and color information from the painting.
Machine learning and neural networks can be leveraged to provide particularly powerful tools for performing style transfer. A computing device, for instance, may train a neural network using machine learning based on a training style image and style features that identify style attributes exhibited by the training style image. The computing device may then process an input image using the trained neural network to transform the input image into a stylized version of the input image that matches various style attributes of the training style image.
In some conventional techniques, convolutional neural networks (CNN) are utilized to migrate style information from style images to input content images. Such techniques typically rely on iteratively processing an input image using a trained CNN to enable the input image to gradually approach the style of a particular style image. These techniques, however, can be time and resource intensive due to their high computational complexity.
Other conventional techniques utilize feedforward networks to perform one-pass style transfer from a style image to an input image. While these techniques may reduce the computational complexity of CNN-based techniques for a single style transfer project, they are typically limited in that separate models must be trained for each style image. This limits the applicability of the techniques for applying different styles. Some more recent techniques have endeavored to adapt feedforward networks to achieve fast style transfer using arbitrary styles, but these techniques typically only achieve coarse style information transfer and fail to capture finer texture features from a style image.